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您的检索式：作者名="Fiona Fulton"
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| 1 | Prediction of voltage distribution using deep learning and identified key smart meter locations显示文摘The energy landscape for the Low-Voltage(LV)networks is undergoing rapid changes.These changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(i.e.adoption of micro-renewables)and demand side(i.e.electric vehicle charging).The previously passive‘fit-and-forget’approach to LV network management is becoming increasing inefficient to ensure its effective operation.A more agile approach to operation and planning is needed,that includes pro-active prediction and mitigation of risks to local sub-networks(such as risk of voltage deviations out of legal limits).The mass rollout of smart meters(SMs)and advances in metering infrastructure holds the promise for smarter network management.However,many of the proposed methods require full observability,yet the expectation of being able to collect complete,error free data from every smart meter is unrealistic in operational reality.Furthermore,the smart meter(SM)roll-out has encountered significant issues,with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks.Even with a comprehensive SM roll-out privacy restrictions,constrain data availability from meters.To address these issues,this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits.The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution,even without the use of the high granularity personal power demand data from individual customers. | Maizura Mokhtar Valentin Robu David Flynn Ciaran Higgins Jim Whyte Caroline Loughran Fiona Fulton | 2021 | Energy and AI2021，6，4： | 0 |
| 2 | Identification of patients with pancreatic adenocarcinoma due to inheritable mutation:Challenges of daily clinical practice显示文摘BACKGROUND Identification of germ-line mutations in pancreatic ductal adenocarcinoma(PDAC) could impact on patient/family.AIM To assess the referral pathways for genetic consultations in PDAC.METHODS Electronic records of PDAC patients were reviewed retrospectively. Patients eligible for genetic consultation referral were identified following the European Registry of Hereditary Pancreatitis and Familial Pancreatic Cancer(EUROPAC)criteria.RESULTS Four-hundred patients were eligible. Of 113 patients(28.3%) meeting EUROPAC criteria, 8.8% were referred for genetic opinion. Germ-line mutations were identified in 0.75% of the whole population.CONCLUSIONEarlier referrals and increased awareness may be able to overcome the low rate of successful genetic appointments. | Alexander JP Fulton Angela Lamarca Christina Nuttall Lynne McCallum Rille Pihlak Derek O'Reilly Fiona Lalloo Mairéad G McNamara Richard A Hubner Tara Clancy Juan W Valle | 2019 | World Journal of Gastrointestinal Oncology2019，11，2： | 0 |
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